In manufacturing and processing environments, it is common to optically inspect and sort individual moving articles with automatic optical inspection equipment. Particularly, where high speed belt conveyors move a mass flow of bulk products or articles past an inspection station, the optical inspection equipment can determine optical properties of the articles, and undesirable articles can be separated from the flow with sorting equipment. In many cases, a general sorting system incorporates some form of basic image-processing capabilities. One area where it is especially important to optically inspect and sort a moving stream of bulk products is in the food-processing industry where there is a need to automatically sort food products by visual or optical inspection of the food products to identify food articles having specified desirable or undesirable visual characteristics. Examples include fruits, vegetables, and nuts. Other areas requiring a similar sorting of bulk products or articles includes the sorting of naturally occurring products such as wood chips and aggregate, of manufactured products such as fasteners and formed parts, and of meat products, particularly of quartered or cubed poultry or beef products.
One very effective optical inspection and sorting system is described in U.S. Pat. No. 4,581,632 granted in 1986 (reissued on Sep. 25, 1990 as RE 33,357), assigned to Key Technology, Inc. of Walla Walla, Wash. In such systems, defective products are detected and removed based upon their optical characteristics. Key Technology manufactures and sells a variety of optical-based sorting systems, including systems utilizing color-inspection cameras. Typically, a high speed conveyor belt conveys a wide swath of food articles past an automatic optical-inspection station. An inspection station is provided relative to a random lateral distribution of individual food articles conveyed by the belt. The inspection station identifies undesirable or defective articles and removes them from the product flow.
Technological advances and increasing market demands are resulting in the development of sophisticated sorting machines offering high-resolution true-color Red, Green, and Blue (RGB) cameras and powerful image-processing engines of ever-increasing complexity. This has resulted in concomitant increases in complexity and a corresponding increase in the demands placed on an operator attempting to set up and calibrate a machine for a particular sorting task.
The evolving complexity of sorting systems is further compounded by the fact that most sorting systems, particularly those utilized in the food-processing industry, employ machine operators who have little or no image-processing or machine-vision expertise, and who very often are illiterate. It becomes a daunting challenge for one of these operators to attempt to use any form of general sorting system, let alone a state-of-the-art sorting system.
To lower manufacturing and support costs, sorter-manufacturing companies want to offer a single, generic, do-everything sorting machine that can process a nearly unlimited variety of products. Thus, food-processing plants have not been able to purchase highly specialized sorting machines that are customized or uniquely configured for their specific products. They have typically purchased one of the available generic sorting machines and then did their best to tailor it for their application. Thus, the burden of machine configuration, initialization, and control has been placed squarely on the operator. As newer and more complex sorting machines become available, this burden will be too much for most operators to handle, thereby reducing sorting performance to a level that is not optimal and far below the potential possessed by these newer machines.
Increasingly complex sorting machines generate two parallel needs. The first need is a method for operating such a machine that hides the complexity of the machine from operators while at the same time provides a straightforward way for them to fully utilize the potential of the machine. The second need is an easy-to-use method for allowing an operator to custom-configure the machine for his particular product. This might include tailoring the machine's operator interface to use terms and jargon that are specific to the product being sorted and thus familiar to machine operators as well.
Adding significant complexity to the latest sorting machines are the image-processing capabilities required to properly identify defective pieces based on their color, shape, and/or size, or the locations of defective areas on them. An example of such image-processing capabilities is described in U.S. Pat. No. 5,335,293, also assigned to Key Technology, Inc., hereinafter incorporated by reference. This patent describes an automated quality inspection station for inspecting food products whose quality can be visually ascertained, for classifying component areas of the products being inspected, and for setting process parameters of in-line processing equipment based on the classifications. Video image scans are captured with a frame grabber after which a processor analyzes data stored in the frame to extract color-, shape-, and/or size-related information. In this manner, video images are first characterized based on classification of color values established in a sample calibration pursuant to a determination of the probability of any single color value occurring in a single component type of defect versus any other type of component defect. Then, contiguous groups of similarly characterized pixels are analyzed for shape- and/or size-related information using multi-stage morphological filters and feature-extraction algorithms. Based on this analysis, each individual product piece is classified in either the acceptable category or one of possibly several defective categories.
With the color-characterization technique described above, an operator first identifies samples of pixels for each defect type, then reference curves are automatically created from these samples, and then the operator specifies and adjusts probability scaling factors which are used to scale these reference curves. Each curve represents the probability of a single color value occurring in any single component defect type relative to other defect types. However, correct operation of this system depends significantly upon operator initialization and adjustments. Additionally, a system may have the potential for recognizing millions of different colors, making this aspect of system initialization somewhat complex for an operator.
Fine-tuning of a sorter with the image-processing capabilities described above requires some degree of manual adjustment of a plurality of interacting sorting parameters. Therefore, in order to correctly configure and initialize an optical sorting system, an experienced and capable operator becomes a requirement. Since such an operator is often not available, optimum sorting results are frequently not obtained. Furthermore, when attempting to produce a single machine capable of being reconfigured for a variety of products, additional ambiguities often arise requiring substantially greater configuration and initialization time and complexity at the operator interface. Many of these ambiguities arise even among the characteristics of a common product. For example, when sorting peas with a sorting device, the setup of the machine can become overwhelming because of the variable nature of the incoming product, data-processing constraints, imperfections in obtaining the data upon which decisions are based, and the imprecise manner in which defective articles are separated from the product stream in many sorting devices. These machines often involve trade-offs and compromises for an operator even when attempting to determine optimum operating settings. Hence, the operator's job becomes overly complex not only for setting up sorts between different products, but also for setting up accurate sorts for a particular product.
Therefore, in light of all of the recent developments in sorting systems, there is a need to greatly simplify the operator interface requirements for configuration, initialization, and operation of sorting machines for specific products. Even for sorting applications requiring only the most basic image-processing capabilities, this need is evident. For those applications requiring more advanced image-processing algorithms, this need is magnified. A simple operator interface on a sorting machine should harness the power and sophistication of the sorting machine while protecting the operator from having to understand its inner complexities.
The invention described below provides a method and apparatus for facilitating the configuration and initialization involved in customizing a highly complex sorting machine for specific products and simplifying the operator interface of the sorting machine to hide complexity. In addition, the methods described below adapt themselves to simple adjustments of a plethora of complex sorting parameters when sorting specific products.